Real-time predictive computer program, model, and method

ABSTRACT

A method for predicting a future occurrence of an event involves obtaining a history of prior occurrences of the event. A plurality of variables is created that are associated with the event. Weights are assigned to each variable. An artificial neural network is accessed and trained with the history of past occurrences of the event by comparing an output of the artificial neural network to the past occurrence of the event. The weights are adjusted until the output corresponds to the past occurrence of the event.

RELATED APPLICATION

This non-provisional application claims the benefit of U.S. ProvisionalApplication Ser. No. 60/862,528, entitled “REAL TIME PREDICTIVE COMPUTERPROGRAM, MODEL, AND METHOD,” filed Oct. 23, 2006. The identifiedprovisional application is incorporated herein by specific reference.This application is also a continuation-in-part of U.S. Application Ser.No. 11/678,884, entitled “REAL-TIME PREDICTIVE COMPUTER PROGRAM, MODEL,AND METHOD,” filed Feb. 26, 2007, which is incorporated herein byspecific reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to programs, models and methods forpredicting future occurrences of events. More particularly, theinvention relates to a method for predicting a future occurrence of anevent using a plurality of variables and an artificial neural network.

2. Description of the Related Art

Many organizations attempt to predict future events or trends to moreefficiently and/or economically provide services. For example, medicalfacilities such as hospitals and clinics would like to determinepatient-related information such as length of stay, treatment options,and pharmaceutical needs for a given ailment. Such information can helpthe medical facility plan for issues such as bed space, staffingconcerns, and purchasing and storage of supplies. As a result, costscould be cut by not planning to provide more resources than would benecessary.

Trend forecasting and future event prediction in the past have involvedaccumulating data associated with the subject of the forecasting orprediction. Regression or extrapolation techniques are applied to thedata to find the trend or predict future activity. However, thesetechniques don't take into account variable data connected with theevent.

SUMMARY OF THE INVENTION

The present invention provides a distinct advance in the art of methodsof predicting future occurrences of events. More particularly, theinvention provides a method for predicting a future occurrence of anevent that takes into account a plurality of variable data related tothe event.

One embodiment of the invention is a method of predicting a futureoccurrence of an event. The method begins with obtaining a history ofpast occurrences of the event. Next, a plurality of variables that areassociated with the event are created, and a weight is assigned to eachvariable. An artificial neural network is then created. The artificialneural network is trained with the history of occurrences of the eventby applying the variables associated with the event to the artificialneural network and comparing an output of the network with a pastoccurrence of the event. The method is completed by adjusting theweights of the variables such that the output of the network correspondsto the past occurrence of the event.

An exemplary embodiment of the invention is a method of predicting aworker's compensation injury. The method begins with obtaining a historyof worker's injuries requiring worker's compensation. Next, anartificial neural network is accessed. Then, the artificial neuralnetwork is trained with the history of worker's compensation injuries topredict the next occurrence of a worker's compensation injury.

Another exemplary embodiment of the invention is a method of predictingan acute medical situation. The method begins by obtaining a history ofacute medical situations of a patient. Next, an artificial neuralnetwork is accessed. Then, the artificial neural network is trained withthe history of acute medical situations to predict the occurrence of thenext acute medical situation.

Embodiments of the invention provide many advantages over prior arttechniques. Individual ATM payment cards may be issued to each injuredor ill employee for payment at point of treatment such as doctor'soffices, drug stores, hospitals, and the like. The cards may pay theprovider on a real-time agreed-upon fee schedule which is above theusual discounted insurance company schedule but generally slightly lessthan their usual and customary rate schedule. If a provider meets orexceeds the quality ceiling set for patient care, cost reduction andreduced lost work time, then the provider would be eligible for paymentat their usual and customary rate schedule. The methods of the inventioninclude adjudicating the claims with a significant savings to theinsurer and the provider.

Other aspects and advantages of the present invention will be apparentfrom the following detailed description of the preferred embodiments andthe accompanying drawing figures.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

A preferred embodiment of the present invention is described in detailbelow with reference to the attached drawing figures, wherein:

FIG. 1 is a schematic diagram illustrating some of the elements operableto be utilized by various embodiments of the present invention; and

FIG. 2 is a flow diagram showing some of the steps operable to beperformed by various embodiments of the present invention.

The drawing figures do not limit the present invention to the specificembodiments disclosed and described herein. The drawings are notnecessarily to scale, emphasis instead being placed upon clearlyillustrating the principles of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following detailed description of the invention references theaccompanying drawings that illustrate specific embodiments in which theinvention can be practiced. The embodiments are intended to describeaspects of the invention in sufficient detail to enable those skilled inthe art to practice the invention. Other embodiments can be utilized andchanges can be made without departing from the scope of the presentinvention. The following detailed description is, therefore, not to betaken in a limiting sense. The scope of the present invention is definedonly by the appended claims, along with the full scope of equivalents towhich such claims are entitled.

Methods consistent with the present teachings are especially well-suitedfor implementation by a computing element, such as the computer 10illustrated in FIG. 1. The computer 10 may be a part of a computernetwork that includes one or more client computers and one or moreserver computers interconnected via a communications system 12 such asan intranet, the internet a wireless network, or any othercommunications network. The present invention will thus be generallydescribed herein as a computer program. It will be appreciated, however,that the principles of the present invention are useful independently ofa particular implementation, and that one or more of the steps describedherein may be implemented without the assistance of the computingdevice.

The present invention can be implemented in hardware, software,firmware, or a combination thereof. In a preferred embodiment, however,the invention is implemented with a computer program. The computerprogram and equipment described herein are merely examples of a programand equipment that may be used to implement the present invention andmay be replaced with other software and computer equipment withoutdeparting from the scope of the present teachings.

Computer programs consistent with the present teachings can be stored inor on a computer-readable medium residing on or accessible by a hostcomputer for instructing the host computer to implement the method ofthe present invention as described herein. The computer programpreferably comprises an ordered listing of executable instructions forimplementing logical functions in the host computer and other computingdevices coupled with the host computer. The computer program can beembodied in any computer-readable medium for use by or in connectionwith an instruction execution system, apparatus, or device, such as acomputer-based system, processor-containing system, or other system thatcan fetch the instructions from the instruction execution system,apparatus, or device, and execute the instructions.

The ordered listing of executable instructions comprising the computerprogram of the present invention will hereinafter be referred to simplyas “the program” or “the computer program.” It will be understood bypersons of ordinary skill in the art that the program may comprise asingle list of executable instructions or two or more separate lists,and may be stored on a single computer-readable medium or multipledistinct media. In the context of this application, a “software object”is a programming unit that groups together a data structure (e.g.,instance variables) and the operations (e.g., methods) that can use oraffect that data.

In the context of this application, a “computer-readable medium” can beany means that can contain, store, communicate, propagate or transportthe program for use by or in connection with the instruction executionsystem, apparatus, or device. The computer-readable medium can be, forexample, but not limited to, an electronic, magnetic, optical,electro-magnetic infrared, or semi-conductor system, apparatus, device,or propagation medium. More specific, although not inclusive, examplesof the computer-readable medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a random access memory (RAM), a read-only memory (ROM), anerasable, programmable, read-only memory (EPROM or Flash memory), anoptical fiber, and a portable compact disc (CD) or a digital video disc(DVD). The computer-readable medium could even be paper or anothersuitable medium upon which the program is printed, as the program can beelectronically captured, via for instance, optical scanning of the paperor other medium, then compiled, interpreted, or otherwise processed in asuitable manner, if necessary, and then stored in a computer memory.

The steps of an embodiment of the method that create a model forpredicting a future occurrence of an event utilized by variousembodiments of the present invention are illustrated in the flow diagramof FIG. 2. The method begins at step 20 by obtaining a history of pastoccurrences of the event. Thus, the event has to have already happenedto predict a future occurrence. Embodiments of the present invention arenot operable to be utilized in predicting the occurrence of an eventthat has not already occurred. Furthermore, embodiments of the presentinvention require a minimum of twenty-five occurrences of the event inorder to properly predict a future occurrence of the event. Generally,as the number of prior occurrences of the event increases, so does theaccuracy of the prediction of future occurrences of the event.

The next step 21 of the method is to create a plurality of variablesassociated with the event. Considerations for the creation of variablesinclude but are not limited to: geographical location of the event,environmental conditions during the event, ages of entities involvedwith the event, sizes of entities involved with the event, shapes ofentities involved with the event, physical condition of entitiesinvolved with the event, quantities of entities involved with the event,costs associated with the event, measurable properties of entitiesinvolved with the event, and miscellaneous factors.

Each variable created in step 21 has a weight assigned to it in step 22.The weight is a multiplication factor for the input variable data. Invarious embodiments of the present invention, the weight is a value thatis greater than 0 and less than 1, such that the sum of all weights isequal to 1.

In step 23, an artificial neural network (ANN) is created in variousembodiments. In other embodiments, an already-existing ANN is accessed.In the preferred embodiment, a feed-forward, back-propagate type of ANNis utilized. Other types of ANNs could be utilized as well, such as thedelta bar delta, the extended delta bar delta, and the directed randomsearch.

Once the ANN is created or chosen, the ANN must be trained with thehistory of occurrences of the event. In preferred embodiments, thetraining is accomplished with supervised training. The training beginsas indicated in step 24 with applying the variable data to the ANN. Anoutput of the ANN is compared a past occurrence of the event in step 25.Then in step 26, the weights of the variables are adjusted such that theoutput of the ANN corresponds to the past occurrence of the event. Thetraining is repeated for each occurrence of the event.

The following examples further illustrate the method of predicting afuture occurrence of an event as utilized in various embodiments of thepresent invention.

The method can be used, for example, to create a model to predict afuture occurrence of a worker's compensation injury. In preferredembodiments, the method begins with obtaining a history of at leasttwenty-five prior occurrences of worker's injuries that requiredworker's compensation. Next, a list of variables associated withworker's compensation injuries is created. Weights are assigned to eachvariable. One possible set of variables associated with worker'scompensation injuries is listed in Table 1. Other variables could beadded as experience determines they are relevant to the prediction.

TABLE 1 Listing of variables associated with worker's compensationinjuries Initial Weighting Final Weighting Variables For ANN's For ANN'sPre-morbidity status 7% 8% Post-event status 7% 10%  Post-medicalcondition 7% 8% Hospitalization 7% 10%  Support at home 7% 9%Availability of home care 7% 4% Availability of outpatient services 7%4% Suitability of individual for his job 7% 10%  Availability oflight-duty work 7% 6% Availability of job retraining 7% 8% Potential forlitigation 7% 6% State in which injury occurred 7% 10%  Miscellaneous16%  7% 100%  100% 

Table 1 also shows the initial weights for each variable. Othervariables that are used to train the ANN include, but are not limitedto:

-   -   Barthel Scale—pre-event, on date of accident, every week for 16        week, 1 year follow up    -   VAS scale for pain (1-10) at each Barthel Scale evaluation    -   Types of industry the individual worked in    -   Age, height, weight of the individual prior to the injury    -   General health of the individual prior to the injury    -   Date of injury    -   Type of injury    -   Treatments received for the injury and costs of treatments    -   Worker was hospitalized (yes/no)    -   Worker received surgery (yes/no—If yes, then type of surgery)    -   Worker received physical therapy/occupational therapy/job        retraining (amount of duration)    -   Type and amount of medications worker received    -   Type and amount of home care worker received (duration)    -   Number of days worker not working    -   Was worker able to return to their job    -   Was worker assigned light duty work    -   Total cost of worker compensation injury    -   How the worker would rate their work environment

Once the variables are determined, an ANN is chosen and trained with thehistory of worker's compensation injuries. For each injury, the variabledata is applied and an output of the ANN is compared with the occurrenceof the injury. The weights are adjusted until the output of the ANNcorresponds to the occurrence of each past injury. The final weights forthis example are also listed in Table 1.

In addition to predicting a future occurrence of a worker's compensationinjury, the model could also be used to predict, among other things, ananticipated date of hospital discharge, if the disability will bepermanent, if retraining will be necessary to reenter the workplace, ananticipated time for return to work, type of work status, the need andtiming for rehabilitation services, the need and timing for independentmedical evaluation, and the probability of potential litigation.

Another example in which the method of various embodiments of thepresent invention could be utilized is predicting an acute medicalsituation. In preferred embodiments, the method begins with obtaining ahistory of at least twenty-five prior illnesses. Next, a list ofvariables associated with acute medical situations is created. Weightsare assigned to each variable. One possible set of variables associatedwith acute medical situations is listed in Table 2. Other variablescould be added as experience determines they are relevant to theprediction.

TABLE 2 Listing of variables associated with acute medical situationsInitial Weighting Final Weighting Variables For ANN's For ANN'sPre-morbidity status 6% 3% Post-event status (day 2) 6% 7% Post-eventstatus (day 3) 6% 7% Post-event status (day 4) 6% 7% Post-event status(day 5) 6% 6% Co-morbidities 6% 8% Medications 6% 7% X-rays/diagnostictests 6% 5% Laboratory tests 6% 5% Physical therapy 6% 7% Occupationaltherapy 6% 7% Activities of daily living 6% 7% Surgeries 6% 8% Supportat home 6% 8% Availability of home care 6% 4% Miscellaneous 10%  4%100%  100% 

The initial weighting of the variables is also listed in Table 2. Next,an ANN is chosen and trained with the history of illnesses. For eachillness, the variable data is applied and an output of the ANN iscompared with the occurrence of the illness. The weights are adjusteduntil the output of the ANN corresponds to the occurrence of each pastillness. The final weights for this example are also listed in Table 2.

Using the same acute medical situation predictive model, the method ofvarious embodiments of the present invention could also be used topredict, among other things, length of hospital stay, the length of timepost-discharge services, the need for home health services, outpatienttherapy services, and pharmaceutical needs. The acute medical situationmodel could also be used to predict events for pediatric medical care.

A similar example to the acute medical situation example is using themethod of various embodiments of the present invention to predict anindividual's wellness. The method requires a history of at leasttwenty-five past illnesses and a variable set related to an individual'swellness with initial weights. One possible set of variables associatedwith wellness is listed in Table 3. Other variables could be added asexperience determines they are relevant to the prediction.

TABLE 3 Listing of variables associated with individual wellness InitialWeighting Final Weighting Variables For ANN's For ANN's Current medicalstatus 6% 7% Family history 6% 8% Co-morbidities 6% 7% Laboratory data6% 6% X-ray data 6% 6% Past medical history 6% 8% Weight 6% 8% Height 6%5% Lifestyle 6% 6% Blood pressure 6% 6% Cholesterol level 6% 7% Worker'scompensation injuries 6% 5% Vaccinations 6% 3% Stress level 6% 4%Exercise level 6% 8% Miscellaneous 10%  4% 100%  100% 

An ANN is chosen and trained with the history of illnesses. For eachillness, the variable data is applied and an output of the ANN iscompared with the occurrence of the illness. The weights are adjusteduntil the output of the ANN corresponds to the occurrence of each pastillness. The initial and final weights for this example are listed inTable 3.

In various embodiments, this model can be used to predict future medicalevents and illnesses for an individual, thus anticipating the need forwellness programs, wellness lifestyle changes, and medical careintervention.

Another example in which the method of various embodiments of thepresent invention could be utilized is predicting premature/neo-natalbirths. In preferred embodiments, the method begins with obtaining ahistory of at least twenty-five prior births. Next, a list of variablesassociated with pregnancy is created. Weights are assigned to eachvariable. One possible set of variables associated with pregnancy islisted in Table 4. Other variables could be added as experiencedetermines they are relevant to the prediction.

TABLE 4 Listing of variables associated with premature/neo-natal birthsInitial Weighting Final Weighting Variables For ANN's For ANN's Time ofcomplete blood 12% 10% chemistry analysis Time of exam by OB/GYN 12% 10%Cross-linked collagen diagnosis 12% 20% Prescription of propermedication 12% 20% Commencement of 12% 10% wellness program Cessation ofsmoking 12% 10% Cessation of 12%  9% alcohol consumption Cessation ofdrug use 12%  9% Miscellaneous  4%  2% 100%  100% 

An ANN is chosen and trained with the history of births. For each birth,the variable data is applied and an output of the ANN is compared withthe occurrence of the birth. The weights are adjusted until the outputof the ANN corresponds to the occurrence of each past birth. The initialand final weights for this example are listed in Table 4. The model ofthis example can be used to predict a premature/neo-natal birth, therebyallowing medical staff to take preventative action to avoid such asituation.

Another example in which the method of various embodiments of thepresent invention could be utilized is predicting animal illnesses in aveterinary medical practice. In preferred embodiments, the method beginswith obtaining a history of at least twenty-five prior animal illnesses.Next, a list of variables associated with the veterinary practice iscreated. Weights are assigned to each variable. One possible set ofvariables associated with veterinary practices is listed in Table 5.Other variables could be added as experience determines they arerelevant to the prediction.

TABLE 5 Listing of variables associated with a veterinary medicalpractice Initial Weighting Final Weighting Variables For ANN's For ANN'sNumber of veterinarians 5% 4% Number of vet assistants 5% 3% Size ofanimal clinic 5% 2% Size of client base 5% 5% Size of household base 5%5% Number of large dogs (>=30 lbs) 5% 6% Number of small dogs (<30 lbs)5% 6% Number of cats 5% 6% Number of large-dog surgeries 5% 7% Number ofsmall-dog surgeries 5% 7% Number of cat surgeries 5% 7% Number of officesupport staff 5% 2% Human population of service area 5% 3% Averagecharge per visit 5% 4% Average charge per surgery 5% 7% Average chargeper household 5% 3% Cost of supplies per visit 5% 4% Cost of suppliesper surgery 5% 7% Number of specialty referrals 5% 8% per veterinarianMiscellaneous 5% 8% 100%  100% 

An ANN is chosen and trained with the history of animal illnesses. Foreach illness, the variable data is applied and an output of the ANN iscompared with the occurrence of the illness. The weights are adjusteduntil the output of the ANN corresponds to the occurrence of each pastanimal illness. The initial and final weights for this example arelisted in Table 5.

Another example in which the method of various embodiments of thepresent invention could be utilized is predicting the occurrence of anoil or gas pipeline failure. In preferred embodiments, the method beginswith obtaining a history of at least twenty-five prior pipelinefailures. Next, a list of variables associated with the pipeline iscreated. Weights are assigned to each variable. One possible set ofvariables associated with pipelines is listed in Table 6. Othervariables could be added as experience determines they are relevant tothe prediction.

TABLE 6 Listing of variables associated with pipelines Initial WeightingFinal Weighting Variables For ANN's For ANN's Manufacturer of pipe 7% 8%Type of pipe 7% 10%  Diameter of pipe 7% 8% Location of pipe 7% 10% Type of valve 7% 9% Manufacturer of valve 7% 4% Diameter of valve 7% 4%Location of valve 7% 10%  Topography 7% 6% Climate 7% 8% Pressure inpipeline 7% 6% Corrosiveness of pipeline contents 7% 10%  Age ofpipeline 7% 16%  Miscellaneous 9% 7% 100%  100% 

An ANN is chosen and trained with the history of pipeline failures. Foreach failure, the variable data is applied and an output of the ANN iscompared with the occurrence of the failure. The weights are adjusteduntil the output of the ANN corresponds to the occurrence of each pastpipeline failure. The initial and final weights for this example arelisted in Table 6. The model of this example could be used to predictwhen and where a future pipeline failure might occur.

Another example in which the method of various embodiments of thepresent invention could be utilized is predicting the future needs foran electrical power distribution network. In preferred embodiments, themethod begins with obtaining an analysis of at least twenty-five powerdistribution networks. Next, a list of variables associated with powerdistribution networks is created. Weights are assigned to each variable.One possible set of variables associated with power distributionnetworks is listed in Table 7. Other variables could be added asexperience determines they are relevant to the prediction.

TABLE 7 Listing of variables associated with power distribution networksInitial Weighting Final Weighting Variables For ANN's For ANN's Currentare served 7% 4% Population of current service area 7% 5% Populationgrowth 7% 9% of current service area Current usage by household 7% 10% Projected future usage 7% 8% by household Age of existing service area7% 6% network Industrial growth potential of 7% 8% current service areaGeographic growth of service area 7% 9% Growth demand of new 7% 10% service area Cost of enhancing existing 7% 4% service area Revenuesgenerated by 7% 5% enhancing existing service area Cost of enhancing new7% 6% area service Revenues projected from 7% 10%  enhanced new servicearea Projected life of new 7% 5% service area network Miscellaneous 2%1% 100%  100% 

An ANN is chosen and trained with the data from existing powerdistribution networks. For each network, the existing power distributionnetwork data is applied and an output of the ANN is compared with thenetwork data. The weights are adjusted until the output of the ANNcorresponds to the existing power distribution network data. The initialand final weights for this example are listed in Table 7.

In various embodiments, this model can be used to predict how large ofan area can be served with the current electrical power distributionnetwork. The model can also predict the enhancement of the currentnetwork as well as projecting the next power distribution grid.

Although the invention has been described with reference to thepreferred embodiment illustrated in the attached drawing figures, it isnoted that equivalents may be employed and substitutions made hereinwithout departing from the scope of the invention as recited in theclaims.

1. A method of predicting a workers compensation injury, the methodcomprising the steps of: obtaining a history of injuries that requiredworker's compensation; accessing an artificial neural network; trainingthe artificial neural network with variables including: types ofindustry the individual worked in, age, height, weight of the individualprior to the injury, general health of the individual prior to theinjury, date of injury, and type of injury; utilizing the trainedartificial neural network to predict the occurrence of the next worker'scompensation injury.
 2. The method of claim 1, wherein the methodfurther includes assigning a weight to each variable.
 3. The method ofclaim 2, wherein the method further includes adjusting the weights ofthe variables.
 4. The method of claim 1, wherein the method furtherincludes comparing an output of the network with a past occurrence ofthe worker's compensation injury.
 5. The method of claim 1, wherein thevariables further include Barthel Scale-pre-event, on date of accident,every week for 16 weeks, 1 year followup, and VAS scale for pain (1-10)at each Barthel Scale evaluation.
 6. The method of claim 1, wherein thevariables further include treatments received for the injury and costsof treatments.
 7. The method of claim 1, wherein the variables furtherinclude whether worker was hospitalized.
 8. The method of claim 1,wherein the variables further include whether worker received surgery.9. The method of claim 1, wherein the variables further include whetherworker whether worker received physical therapy/occupational therapy/jobretraining.
 10. The method of claim 1, wherein the variables furtherinclude the type and amount of medications worker received.
 11. Themethod of claim 1, wherein the variables further include the type andamount of home care worker received.
 12. The method of claim 1, whereinthe variables further include the number of days worker not working. 13.The method of claim 1, wherein the variables further include whetherworker able to return to their job.
 14. The method of claim 1, whereinthe variables further include whether worker assigned light duty work.15. The method of claim 1, wherein the variables further include thetotal cost of worker compensation injury.
 16. The method of claim 1,wherein the variables further include the rating worker would give theirwork environment.
 17. A method of predicting a workers compensationinjury, the method comprising the steps of: obtaining a history ofinjuries that required workers compensation; accessing an artificialneural network; training the artificial neural network with variablesincluding: Barthel Scale—pre-event, on date of accident, every week for16 weeks, 1 year followup, VAS scale for pain (1-10) at each BarthelScale evaluation, types of industry the individual worked in, age,height, weight of the individual prior to the injury, general health ofthe individual prior to the injury, date of injury, type of injury,treatments received for the injury and costs of treatments, whetherworker was hospitalized, whether worker received surgery, whether workerreceived physical therapy/occupational therapy/job retraining, type andamount of medications worker received, type and amount of home careworker received, number of days worker not working, whether worker ableto return to their job, whether worker assigned light duty work, totalcost of worker compensation injury, and rating worker would give theirwork environment; and utilizing the trained artificial neural network topredict the occurrence of the next workers compensation injury.